Technological developments designed to harvest solar energy have greatly accelerated in recent years. One particular aspect of this has been the use of photovoltaic (PV) modules consisting of a multitude of interconnected photovoltaic cells. Each solar cell produces about 0.6V when illuminated by sunlight, thus with a module of 72 such cells, can yield a total of about 40 volts or about 300 Watts. Arrays of modules form panels, mounted on a support system usually referred to as a rack. This includes not only roof-mounted systems but also commercial and large-scale ground-mounted utility plants designed to introduce the generated electrical power into the grid.
While this technology has become the world standard for solar energy harvesting, much of the technical aspects of PV implementations are still in rapid development. Solar cell technology is far from being settled, as efforts are being made to improve the amount of energy collected per unit dollar of investment. Thus every aspect of the PV design and its implementation in the field is under scrutiny in order to minimize the cost-to-benefit ratio. With rapid declines in the prices of solar modules in recent years, the cost of solar modules has become a significantly smaller fraction of the total cost of the photovoltaic power system. Besides the cost of modules, important factors can include: costs associated with the land procurement; labor for design, construction and management; materials for the electrical components of the solar plant itself and those components required to integrate the power into the local electrical grid, as well as the mechanical infrastructure; operations and maintenance (O&M) costs, cost of capital (e.g., loan repayments). These are all variables that can be potentially adjusted. More non-controllable factors, which can impact the productivity and profit of a PV solar plant, include the solar insolation at a given position on the Earth (latitude and longitude), geological conditions in soil or terrain, and the meteorological conditions, not only on a seasonal or annual basis, but perhaps on a diurnal basis as well.
To aid in understanding the overall problem, a few examples might help to clarify the situation and its inherent complexity. For instance, an obvious adjustable variable is the tilt (elevation of a PV module) towards the Sun. A lower tilt may produce less power but it results in such reduced wind loading that the structural costs are reduced. Moreover, a lower tilted panel also is subject to larger accumulation of dirt and debris. Additionally, a lower tilted panel could be subject to greater snow loading and thus greater structural costs. These are all considerations that impact the design of the PV panel structural support system.
Another variable that can be changed is the effectiveness of land utilization given how modules are organized at site. In other words, a variable that can change is the module packing density or ground coverage ratio (GCR: defined as panel dimension projected onto the ground divided by panel row spacing), as it can be optimized for maximum energy harvest or maximum return on investment. Another possibility is to align solar modules with different azimuths. This is particularly important as it can optimize the internal return of return (IRR) given a grid utility's Time of Day (TOD) payment factors. Since utilities pay more for electricity at peak times, they provide TOD tables that weight the value of the power produced to match their hourly demand. An internal-rate-of-return (IRR) maximized fixed structure will, when viewed as an entire array, be aimed at the most valuable azimuthal angle. For example, a utility in Arizona might want more of its production after 4 PM, while a utility a few hundred miles west in California could want more of its production between noon and 2 PM, and the optimal array for either of these scenarios would have a different azimuth.
One of the many technical factors that can impact a project financially is that of the electrical and mechanical module support system, impacted by local conditions such as wind, snow, soil conditions, and seismic conditions as well. There are three basic groups of support system designs that vary in their ability to adjust the position of the module towards the instantaneous position of the Sun. The first group involves a static rack that provides no instantaneous adjustment, and the position is permanently fixed. However, such racks can be seasonally adjusted but remain fixed in daily operation.
The second and third groups involve tracking the Sun to collect more solar light, accounting for the fact that the Sun does not move in a constant declination. Such a requirement has financial implications that are not readily obvious from a simplistic assessment of a PV power plant design, as its implementation and the form of this implementation yields opposing results.
The second possible module support system method is to move the module or panel along an axis (single-axis tracking), tracking the Sun as it moves across the sky in its diurnal arc. There are several implementations of this type of tracking: vertical single axis tracker; horizontal single-axis tracker; and equatorial or polar tracker. Each one of these variations of single-axis tracking possesses advantages and disadvantages and for particular needs and sites. An obvious advantage is that more solar power is collected. Disadvantages include either increased shading or decreased GCR, and greater costs in maintenance and/or replacement due to the larger number of mechanical and/or electrical parts. Even a specific type of single-axis tracker can have different tracking ranges, tilt angle, and tracking accuracies that depend on tracking methods.
The third method is dual-axis tracking, which provides movements that follow the diurnal and seasonal paths of the Sun. Each axis requires some sort of mechanism to provide the movement, normally by gearing and a motor. This type of tracking provides the most collected solar light, but because of the potential of shading of one panel to another at certain times of the day, there is a reduction in the attainable solar power collected. Thus a design with dual-axis tracking may require a greater separation between panels to reduce losses imposed by shading of one panel by another at extreme hour angles. Thus GCR is reduced.
Modules are usually warranted for several decades, but tracking systems that are robust enough to last a similar period of time, requiring no or little maintenance, are rare and expensive. Thus long-term maintenance of the mechanics of tracking along with availability risks have to be considered as part of the overall problem of designing a solar PV plant.
A static module support system eliminates the need for maintenance associated with a tracking system, and thus presents a viable option for optimal system designs, particularly in areas where O&M labor costs are high or areas that are closer to the equator where dual-axis tracking systems offer less of a performance advantage. Thus dual-axis tracking is more beneficial at more northerly locations. There are a great variety of static support systems, each of which possesses advantages and disadvantages.
A common approach for static, or fixed, racks is to cover an area with linear configurations of module arrays, with the long axis normally parallel to, or nearly so, a line of latitude. The modules are then oriented so that the normal to the PV module surface is oriented to maximize incident flux for a particular time of the year, at the declination when the sun crosses the local meridian. This is the tilt angle of a module. This traditional and commonplace approach makes the construction simple as a replication of a single design, such as the module support, is repeated as needed.
If a static support rack is chosen, then a decision is required to set the tilt and azimuth angles to particular values given a value of GCR. The tilt and azimuth angle would achieve a desired goal, such as maximizing energy that could be achieved over a certain period of time or during a certain part of the day. Importantly, other adjustable parameters include, height above the ground (or ground clearance), spacing between the rows of modules, and bank (permanent EW angle bias of a module, e.g., EW tilt). These all add complexity to designing a static support rack. For example, increasing ground clearance (height) increases structural and electrical costs but saves operational costs for the life of the project, and increasing row spacing decreases shading and increases system performance but also decreases GCR, while increasing electrical, module packing density, and operational costs.
In the case of bank, a geographic variation would be possible if the terrain of the site possesses irregularities. Moreover, it can be non-zero if there are other non-controllable aspects involved such as shading and persistent diurnal meteorological conditions.
Other local variations can also influence IRR or other figures-of-merit, such as soil conditions, site undulations, and local geographical features such as forests or buildings. Any and all of these can influence the energy collected. Grade or soil characteristics will also influence the selection of foundation or rack design. Costs that are affected by site-specific features are also important to take into consideration—for example, sites with sandy soil generate far less skin friction for ground penetrating piles than do sites with clay-like soil, and thus require far longer piles and higher costs.
Foundation type is one of many other variables that need to be considered as part of the design. These include above-ground concrete ballast, subterranean poured concrete foundation, post/pile driven, or ground screws. Bedrock that is just below the basal subsoil will require a different mounting approach.
Sites that have high load bearing characteristics and, for example, 5-12 feet of penetrable soil, are well suited for a pile driven module rack. On the other hand, if sites cannot endure surface penetration, landfills for example where landfill caps cannot be disturbed, then this will force the use of more costly above-ground footings, more costly above-ground electrical wiring conduits, and more costly inverter installations. All of these factors must be taken into account.
Other design variables are the costs and types of wirings for direct current (DC) and separately for alternating current (AC). Costs and warranties for inverter types, switchgear, power management systems, cooling equipment, combiner boxes, DC-DC optimizers, and energy storage facilities will also affect the optimum solution. A mechanical system design with higher energy production and lower cost than an alternative mechanical system design might require an electrical system with higher cost than the electrical system needed by that alternative mechanical design.
Modules are often clustered in strings in physical and electrical combinations that are sized according to GCR requirements, power requirements, and inverter input (as well as other variables, well-known to the person skilled in the art). For example, photovoltaic designers typically combine 18 to 21 modules into a string, and combine these strings to produce an array or panel. However, common practice is subject to change when other options, in concert with other parameters or variables, are taken into account.
Another example would be the design of a solar array with different rows of modules having different mechanical and electrical design parameters to optimize one user's needs. No current approach addresses this user or is able to find the optimum solution that could have, for example, the rows of panels on the peripheries of the array would require a different structural quality and wall thickness than the interior rows. This type of structurally optimum solution leverages the fact that interior rows see reduced wind loading due to the presence of the surrounding rows. Similarly no current approach is able to design different strings with different module and wiring configurations, or to design back rows that can have higher tilts due to lack of shading concerns.
Computer-aided performance optimizations are also in a rudimentary form. For instance, consider the literature available on “tilt” optimization. Some suggest tilt angles that are simple functions of latitude, but this fails to consider site specifics (such as typically cloudy winters or morning fog in summer). At best, some consultants offer to provide “tilt optimization” services as a detailed, hands-on process. No other tools allow one to run a “batch” of simulations at various tilt angles to determine the best angle from a performance perspective.
While all of these approaches work to a degree, they are either overly simplistic or laborious, and thus time consuming. Given the somewhat large number of variables and constraints involved, no design of a solar power plant or array can be accomplished in even a finite amount of time that is reliably accurate, detailed, and optimized (financially or otherwise).
There are several existing tools that will help facilitate the look of what a PV plant will look like when imposed upon, say, an image from Google Maps or other equivalent mapping service. Boundary points are interactively defined and with various parameters selected (row spacing, PV module type) the program will present a first-order look of a design. There is neither any optimization nor any automated design production in such approaches.
A few services (e.g., Helioscope from Folsom Labs; Helios3D from Schletter GmbH) exist to define a visual design automatically based on a user's input. However, these methods are suboptimal for the extensive task to be performed. A basic design is derived and the parts replicated ad nauseum; this is essentially multiplying a number of parts for a Bill-of-Materials (BOM) for a solar “kit” to fill the desired geographic area or capacity needs. There is no direct customization of the parts or configuration of the modules. These attempts at design culminate in a single fixed design.
In addition to these visual design programs, there are a variety of PV plant simulators that have been developed. The best and most useful is that of the System Advisor Model (SAM) developed by the US National Renewable Energy Laboratory. It provides a simulation based upon extensive input parameters.
In current practice, simulations are already used to determine the performance of a design. Unfortunately, the process of refining that design is not automated. For example, there is no optimization within current standard simulations; that is, current practice does not simulate multi-variable mechanical and electrical design parameters, multi-dimensional considerations including meteorological, environmental and cost factors, or a specific solar project's optimization goals.
At present the industry lacks sufficiently detailed tools to aid in the evaluation of the plurality of inputs and outputs of various system designs, given the large number of variables or parameters involved. The number of permutations of the design parameters rapidly becomes so large that it is infeasible to attempt to “brute force” an optimal solution by simply evaluating the system model for every possible permutation. For this reason, the solar industry typically uses excessive standardization in photovoltaic project design and ignores more effective solutions to handle large numbers of permutations. This is in stark contrast to the lack of granularity in sensitivity analysis that can be achieved with the standard few iterations of a human PV design engineer or team of engineers.